Abstract

The present study investigates changes in the location, frequency, intensity, and dynamical processes of North Atlantic extratropical cyclones with warming consistent with the IPCC Fifth Assessment Report (AR5) representative concentration pathway 8.5 (RCP8.5) scenario. The modeling, analysis, and prediction (MAP) climatology of midlatitude storminess (MCMS) feature-tracking algorithm was utilized to analyze 10 cold-season high-resolution atmospheric simulations over the North Atlantic region in current and future climates. Enhanced extratropical cyclone activity is most evident in the northeast North Atlantic and off the U.S. East Coast. These changes in cyclone activity are offset from changes in eddy kinetic energy and eddy heat flux. Investigation of the minimum SLP reached at each grid point reveals a lack of correspondence between the strongest events in the current and future simulations, indicating the future simulations produced a different population of storms. Examination of the percent change of storms in the storm-track region shows a reduction in the number of strong storms (i.e., those reaching a minimum SLP perturbation of at least −51 hPa). Storm-relative composites of strong and moderate storms show an increase in precipitation, associated with enhanced latent heat release and strengthening of the 900–700-hPa layer-average potential vorticity (PV). Other structural changes found for cyclones in a future climate include weakened upper-level PV for strong storms and a weakened near-surface potential temperature anomaly for moderate storms, demonstrating a change in storm dynamics. Furthermore, the impacts associated with extratropical cyclones, such as strong near-surface winds and heavy precipitation, strengthen and become more frequent with warming.

1. Introduction

Extratropical cyclones are critical players in Earth’s climate system, transporting heat and moisture poleward in order to reduce the equator–pole temperature gradient (Oort and Vonder Haar 1976; Zhang and Rossow 1997; Fasullo and Trenberth 2008). These systems are also major contributors to our everyday weather and can be associated with strong winds and heavy precipitation, which are often damaging and contribute to societal and economic distress. For example, European windstorms Xynthia in 2010 and Gudrun in 2005 both resulted in over $1 billion (over €890 million) in damage (Cipullo 2013; Liberato et al. 2013). In the United States, the Superstorm of 1993 caused $2 billion (€1.8 billion) in property damages and affected over 90 million people (Kocin et al. 1995). On the other hand, some regions heavily rely on the precipitation from these systems for water supplies and livelihoods, such as agriculture. For these and other reasons, it is vital to understand how the distribution, frequency, and intensity of extratropical cyclones will change with a changing climate.

There is a general consensus that a warming climate will result in an overall reduction in the number of extratropical cyclones in the Northern Hemisphere during the winter by the end of this century (e.g., König et al. 1993; Lambert 1995; Carnell et al. 1996; Beersma et al. 1997; Zhang and Wang 1997; Carnell and Senior 1998; Geng and Sugi 2003; Bengtsson et al. 2006; Lambert and Fyfe 2006; Pinto et al. 2007; Teng et al. 2008; Bengtsson et al. 2009; Ulbrich et al. 2009; Catto et al. 2011; McDonald 2011; Mizuta et al. 2011; Chang et al. 2012; Eichler et al. 2013; Zappa et al. 2013). Regional changes, however, may differ from this general trend (e.g., Schubert et al. 1998; Pinto et al. 2009; Colle et al. 2013). In the North Atlantic, the projected changes in cyclone frequency are often related to changes in baroclinicity. The enhanced near-surface warming at high latitudes, known as polar amplification, results in a reduction of the meridional equator–pole temperature gradient in the lower troposphere, thus decreasing the available potential energy for extratropical cyclones. This reduction of baroclinic energy hinders the development of cyclones, therefore reducing the number of storms throughout the basin (König et al. 1993; Zhang and Wang 1997; Geng and Sugi 2003; Bengtsson et al. 2009; Catto et al. 2011; McDonald 2011; Mizuta et al. 2011; Eichler et al. 2013). On the other hand, enhanced tropical upper-tropospheric warming strengthens baroclinicity aloft, which is conducive for synoptic activity (Mizuta et al. 2011). Furthermore, an increase in water vapor could result in more efficient poleward heat transport by extratropical cyclones, which suggests a decrease in the number or strength of eddies needed to move energy poleward (Zhang and Wang 1997; Bengtsson et al. 2006). Because of this expected increase in moisture, it is important for modeling studies to use sufficiently fine grid spacing to resolve diabatic processes in extratropical cyclones, thus improving on the limitations of coarser-resolution general circulation models (GCMs) that may not adequately represent significant aspects of extratropical cyclones, such as storm intensity or frontal structure (Beersma et al. 1997; Bader et al. 2011; Willison et al. 2013, hereafter W13).

Across the northeast North Atlantic, an increase in the number of extratropical cyclones has been projected (Schubert et al. 1998; Geng and Sugi 2003; Ulbrich et al. 2008; Della-Marta and Pinto 2009; Pinto et al. 2009; Catto et al. 2011; Harvey et al. 2012; Zappa et al. 2013). This regional increase can be attributed to a minimum in sea surface temperature warming to the south of Greenland, resulting in an enhancement of the temperature gradient and associated baroclinicity in that area (Carnell and Senior 1998; Bengtsson et al. 2006; McDonald 2011). Additionally, in a subset of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs, Colle et al. (2013) found a 10%–20% increase in cyclone activity off the East Coast of the United States by the late twenty-first century. This increase could be attributed to enhanced latent heat release, rather than an increase in baroclinicity, as proposed for the northeast North Atlantic (Colle et al. 2013; Marciano et al. 2015). Potential enhancement of extratropical cyclone activity in these regions has implications for densely populated areas, such as cities along the Atlantic Coast, the British Isles, and other areas of northwestern Europe.

How the intensity of extratropical cyclones will change with a future climate remains an unsettled question (Bader et al. 2011; McDonald 2011; Feser et al. 2015). Several studies find a projected increase in the number of intense storms, while others suggest a decrease in such events or find little change in their frequency over the Northern Hemisphere during the winter. Generally, studies considering cyclone strength in terms of minimum central sea level pressure (SLP) or precipitation show a projected increase in extreme events (e.g., Carnell et al. 1996; Sinclair and Watterson 1999; Knippertz et al. 2000; Bengtsson et al. 2009; Champion et al. 2011; McDonald 2011; Mizuta et al. 2011; Mizuta 2012; Booth et al. 2013; Zappa et al. 2013; Chang 2014; Lombardo et al. 2015). Studies using vorticity, minimum SLP perturbation, or near-surface wind speed, on the other hand, find a projected decrease in the strongest storms (e.g., Sinclair and Watterson 1999; Geng and Sugi 2003; Catto et al. 2011; McDonald 2011; Chang et al. 2012; Zappa et al. 2013; Chang 2014). Zappa et al. (2013), for example, found a reduction in cyclones exhibiting strong 850-hPa relative vorticity and winds by the end of the century, but an increase in the most intensely precipitating storms. Similarly, Champion et al. (2011) showed an increase in extreme precipitation events but no significant change in vorticity or wind speeds.

Given these uncertainties and conflicting results, the aim of this research is to improve our understanding of one of our most influential weather phenomena. To do so, high-resolution simulations of 10 winter seasons in current and future climates are analyzed to investigate how and why the location, frequency, and intensity of the broad spectrum of storms located primarily in the North Atlantic storm-track region vary with a changing climate. This paper is the latest in a series of publications related to the effect of climate change on extratropical cyclones and the North Atlantic storm track. W13 examined current-day regional simulations using the Weather Research and Forecasting (WRF) Model and showed that the feedback between cyclone intensification and latent heat release was stronger in the high-resolution simulations, highlighting the need for finer grid spacing to better resolve diabatic effects. Marciano et al. (2015) analyzed 10 high-impact wintertime extratropical storms that affected the U.S. East Coast from 1981 to 2010 in current and future environments and found an enhancement in diabatic potential vorticity (PV), precipitation, and low-level winds as well as a reduction in storm minimum SLP in the future. Analysis of 10 winter seasonal simulations in current and future climates by Willison et al. (2015, hereafter W15) showed enhanced eddy kinetic energy (EKE) and 850-hPa eddy heat flux over the eastern North Atlantic. This signal was more prominent at higher resolutions. Here, W15 took a climate dynamics approach to investigating changes to the North Atlantic storm track rather than focusing on smaller, storm-scale features.

A limitation of Marciano et al. (2015) is the focus on a specified and limited sample of storms; all were current-day “blockbuster” events. It is not guaranteed, however, that the high-impact events of the present will correspond directly to high-impact events in the future, even using the pseudo–global warming (PGW) approach. To investigate this, we found the minimum SLP reached at each grid point over the 10-yr period simulated by the PGW simulations of W15 in current and future climates. Figure 1 shows a time series of the number of grid points achieving their minimum sea level pressure at each time over the 10 winter seasons; the spikes indicate the occurrence of an intense system. When the blue (current) and red (future) spikes are collocated, this most likely indicates that the same intense storm occurred in both simulations (e.g., around t = 575). Conversely, when a blue or a red spike occurs independently, it suggests that that particular intense storm occurred in only one or the other simulation. Figure 1 illustrates that, despite the same pattern and variability imposed on the lateral domain boundaries, the future simulation produced a different population of storms; while some intense storms correspond directly between the current and future simulations, others did not, suggesting that the most intense present-day events are not necessarily the most intense events of the future, even in the presence of similar synoptic patterns.

Fig. 1.

Number of grid points per day reaching the minimum sea level pressure: current (blue) and future (red). The top abscissa shows the running number of days in all 10 simulations. The bottom abscissa marks the start of each season.

Fig. 1.

Number of grid points per day reaching the minimum sea level pressure: current (blue) and future (red). The top abscissa shows the running number of days in all 10 simulations. The bottom abscissa marks the start of each season.

Therefore, to improve and expand on previous work, we have implemented a Lagrangian tracking algorithm to the simulations of W15 and utilized a storm-relative composite analysis approach, as in Marciano et al. (2015), to investigate changes in storm-scale features across a broad sample of storms. A departure from the analysis of Marciano et al. (2015) is that in a decade of freely running, albeit regional, simulations, the model is free to choose which storms become the strongest and most significant in their impact. While the simulations analyzed here are those of W15, our application of feature-tracking methods allows us to determine changes, due to global warming, in the distribution of storms that will have the greatest impact. As will be shown below, these changes do not necessarily correspond to changes in Eulerian quantities, such as eddy heat flux and eddy kinetic energy, that are of importance for the storm track and general circulation. Thus, a unique contribution of this work is the application of feature-tracking to these same simulations, revealing additional, societally important information and insight.

The paper continues with section 2 describing the model configurations for the seasonal simulations, as well as the cyclone-tracking technique and methods for analysis. An analysis of changes in North Atlantic extratropical cyclones due to climate change is presented in section 3. Section 4 includes a discussion of results, and last, concluding remarks and ideas for future work are presented in section 5.

2. Data and methods

a. Seasonal simulations

The seasonal simulations analyzed in this study were conducted using the WRF Model (Skamarock et al. 2008), version 3.4.1. The model domain spans the North Atlantic region (Fig. 2) with a horizontal grid spacing of 20 km to ensure that storm-scale features, such as those affected by latent heat release, are better resolved than at typical GCM resolutions on the order of 100 km (W13). These seasonal simulations span 24 December–7 April (6 April for leap years) for the years 2002–11 to simulate 10 January–March (JFM) winters; output is written at 6-h intervals. The Global Forecast System Final Analysis (GFS-FNL; NCEP/NWS/NOAA/U.S. Department of Commerce 2000) with 1° × 1° horizontal grid spacing is used for initial and lateral boundary conditions with sea surface temperatures (SSTs) updated weekly with the 0.5° × 0.5° real-time, global SST analysis (RTG SST; Thiébaux et al. 2003). The Zhang–McFarlane (ZM) scheme handles convective parameterization, while the microphysics and planetary boundary layer are represented by the WRF single-moment 6-class microphysics scheme (WSM6) and Mellor–Yamada–Janjić (MYJ) scheme, respectively. Longwave and shortwave radiation is handled by the Community Atmospheric Model (CAM) schemes and the Noah land surface model represents the land surface.

Fig. 2.

The entire plot area corresponds to the WRF Model domain for seasonal simulations. The main storm track area used for composite analyses is outlined in red.

Fig. 2.

The entire plot area corresponds to the WRF Model domain for seasonal simulations. The main storm track area used for composite analyses is outlined in red.

A PGW approach (e.g., Schär et al. 1996; Frei et al. 1998; Kimura and Kitoh 2007; Sato et al. 2007; Hara et al. 2008; Kawase et al. 2009; Rasmussen et al. 2011; Mallard et al. 2013) is taken to simulate the winters of 2002–11 in a future thermodynamic environment. This technique involves adding temperature changes derived from a subset of CMIP5 models under the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) representative concentration pathway 8.5 (RCP8.5) scenario to the initial and lateral boundary conditions for WRF. Relative humidity is assumed constant, resulting in moisture scaling with the Clausius–Clapeyron relation (Allen and Ingram 2002). The carbon dioxide (CO2) concentration in the CAM radiation scheme is adjusted to 936 ppm, consistent with the 2100 concentration level projected by RCP8.5. Further information regarding the formulation of the PGW simulations is provided by W15. A limitation of the PGW technique is the assumption of similar synoptic variability on the lateral boundaries in the current and future simulations. Previous research has found conflicting results concerning this question (James 2006; Salathé et al. 2008; Brewer and Mass 2016). Additionally, constraining the boundary conditions of the model can exert an influence on the position of the North Atlantic storm track within the domain. Furthermore, this technique guarantees that similar upper-level triggers for cyclone development will be available at the upstream boundary of the domain. Once triggered, however, the disturbances within the domain are allowed to dynamically progress throughout the simulation. Because the PGW method also involves adding temperature changes to an analyzed high-resolution SST field, a benefit of this approach is the maintenance of realistic SST gradient magnitudes, which can affect cyclone development and intensification (e.g., Inatsu et al. 2003; Brayshaw et al. 2011; Booth et al. 2012; Hirata et al. 2016). Another advantage of the PGW technique is that natural variability at the model boundaries is consistent between the current and future simulations, allowing the changes seen in the future simulations to be considered robust compared to natural variability. Therefore, despite the caveats discussed, the PGW approach allows for a controlled simulation isolating the effects of altered thermodynamics associated with climate change on extratropical cyclones (Lackmann 2013; W15).

b. Cyclone tracking

Eulerian and Lagrangian methods are two different approaches commonly used when analyzing cyclones and storm tracks. Eulerian methods typically use temporal bandpass filtering to isolate synoptic scale activity, thus capturing information on the storm track (e.g., Blackmon 1976; Chang 2013). While Eulerian techniques are able to analyze large amounts of data quickly, they combine the effects of anticyclones and cyclones, which could be problematic for studies focusing on just one phenomenon or the other (Schubert et al. 1998; Raible et al. 2008). Moreover, features of specific storms are unable to be captured by Eulerian methods (Anderson et al. 2003; Pinto et al. 2007). Lagrangian techniques (e.g., Murray and Simmonds 1991; Hodges 1994; Blender et al. 1997; Bauer and Del Genio 2006) involve cyclone identification and tracking and offer the ability to assess characteristics of individual cyclones, including duration and intensity (Schubert et al. 1998; Teng et al. 2008).

Here, we utilize a Lagrangian tracking technique, the modeling, analysis, and prediction (MAP) climatology of midlatitude storminess (MCMS) algorithm of Bauer and Del Genio (2006), to identify and track cyclones in the current and future 10-yr winter seasonal simulations. Because of the high resolution of this dataset, other meteorological fields, such as 850-hPa relative vorticity, can be noisy, thus causing difficulty in feature tracking (Blender et al. 1997; Sickmöller et al. 2000); therefore, cyclones in this study are identified and tracked as depressions in SLP.

Because the MCMS routine requires a uniform latitude and longitude grid, the seasonal WRF output is remapped onto a grid with 0.183° latitude and 0.292° longitude spacing. Once cyclone tracks are identified by MCMS, a series of filters is applied to remove erroneously identified tracks. The minimum lifetime filter ensures that all tracks last for at least 24 h, the minimum SLP filter requires that each track reaches a minimum SLP of 1010 hPa or less, and the minimum travel distance filter designates that tracks must extend for at least 200 km. If the aforementioned conditions are satisfied, information for each track is recorded and stored for analysis. Further details on how MCMS identifies cyclone centers and tracks are provided by Michaelis (2015, section 2.3.3).

Potential spurious maxima in track density occurred over certain areas of isolated topographic peaks, and these centers were more common in the present-day than future simulations. Because of the strongly localized nature of these changes, it is suspected that they are artifacts of discrepancies in the computation of SLP and, therefore, are not representative of the storms of interest. Given that our primary interest in this work is the North Atlantic storm track, these areas were excluded from our analysis and are masked out in the track density plots.

Fig. 3.

Difference in time-averaged SLP (hPa) between future and current simulations (future − current). Contours are shaded every 1 hPa.

Fig. 3.

Difference in time-averaged SLP (hPa) between future and current simulations (future − current). Contours are shaded every 1 hPa.

c. Storm-relative compositing

To elucidate dynamical changes in extratropical cyclones, a storm-relative compositing technique was used to compare current-to-future changes in storm-scale features across multiple intensity regimes. A benefit of compositing events is that it allows for common characteristics among storms of certain intensities to be identified. Differences in storm size, frontal position, track lifetime, or propagation speed, however, result in the distortion of some features. Overall, the benefits of storm-relative compositing outweigh the limitations, and this technique has been successfully used in previous work to analyze climate change impacts on extratropical cyclones (e.g., Field and Wood 2007; Perrie et al. 2010; Marciano et al. 2015).

To generate the composites, cyclones were first separated into three intensity regimes: strong, moderate, and weak. Similar to McDonald (2011), storms reaching peak intensity within the main storm-track region are categorized into these regimes using the present-day 5th and 95th percentiles of minimum SLP perturbation (i.e., the difference between the instantaneous SLP and the time-averaged SLP, calculated as the average over the 10 winter seasons, at each point). This criterion is more selective than setting fixed thresholds, thus allowing the tails of the distribution to be emphasized. SLP perturbation was chosen for the intensity metric to eliminate influences of changes in the low-frequency background SLP field (Fig. 3) on storm intensity. By this definition, strong storms must reach a peak intensity of at least −51 hPa, weak storms reach a peak intensity of at most −6 hPa, and moderate storms comprise the remainder of the sample (i.e., reach a peak intensity between −51 and −6 hPa). While the 5th- and 95th-percentile SLP perturbation thresholds for future storms are not likely to equal those for current storms, the same thresholds were used in the present analysis to categorize storms in the current and future simulations in order to assess structural changes of storms of the same intensities. Weak storms generally favored specific topographic regions (not shown) and therefore are not adequate representations of the storms of interest. As a result, storm-relative composites were generated for only strong and moderate storms.

Composited fields were identified within a 25-gridpoint (~500 km) radius, using the time and location when each storm reached peak intensity as the center point. These 51 × 51 grids were averaged across all storms to create a storm-relative composite of a certain field for that intensity regime. Composites were also generated centered on the location of storms 24 and 12 h prior to reaching peak intensity in order to capture more of the life cycle of each storm. A storm must exist at all three of these times to be included in the composite. From the current simulation, 37 strong and 475 moderate storms are composited; 21 strong and 489 moderate storms are composited from the future simulation. Difficulty in assessing changes in extratropical cyclones due to warming across the entire domain could arise owing to regional variations in storm characteristics. For example, cyclones occurring in the Mediterranean region are typically smaller and weaker than Atlantic cyclones (Bengtsson et al. 2006). Therefore, cyclones reaching peak intensity within the main storm-track region (Fig. 2) are isolated for the composite analyses to focus on changes in extratropical cyclones occurring within that region. Even within the main storm-track area there is potential for regional variation in storm characteristics. For example, Dacre and Gray (2009) noted that storms in the western Atlantic are predominately driven by the interaction between a preexisting upper-level disturbance and a surface disturbance, such as a front. Cyclones that are more diabatically driven, on the other hand, are more frequent in the eastern Atlantic.

Storm-relative composites of the upper-tropospheric potential vorticity (UPV), taken as the 300–200-hPa layer average PV, the 2-m potential temperature anomaly , taken as the deviation from the time-averaged 2-m potential temperature, and the diabatic PV (DPV), taken as the 900–700-hPa layer average PV, are generated to help explain why the changes in storm intensity are occurring using the PV framework. Positive is equivalent to low-level cyclonic PV (Bretherton 1966), and we approximate diabatic PV using the 900–700-hPa layer average, consistent with previous work (Davis and Emanuel 1991; Reed et al. 1993; Marciano et al. 2015).

3. Storm-track region changes with warming

a. Track density

The overall pattern in track density, calculated as the number of cyclone tracks per 1° × 1° area, shows the North Atlantic storm track and a local maximum in track density over the Mediterranean region clearly evident in both current and future simulations (Figs. 4a,b). These patterns of track density for the current climate resemble those from other studies (e.g., Fig. 1 from Lambert 1995; Fig. 4 from Wernli and Schwierz 2006). An area of enhanced future cyclone activity is seen in the northeast North Atlantic, coinciding with the Atlantic storm track. Similar to Colle et al. (2013), an increase in track density is also evident along the immediate U.S. East Coast, albeit this change was not found to be statistically significant (Fig. 4c). Areas of reduced activity include portions of the southwest North Atlantic and the western Mediterranean Sea. Large portions of the track density changes over the storm-track region are found to be statistically significant at the 95% confidence level using the Wilcoxon rank-sum significance test (Fig. 4c). A similar set of 10-yr winter simulations with temperature changes for PGW derived from a subset of CMIP3 GCMs (Cipullo 2013) produced qualitatively similar patterns of track density changes (Fig. 4.3 from Michaelis 2015). The majority of these changes in track density are consistent with those found in previous work (e.g., Fig. 2c from Geng and Sugi 2003).

Fig. 4.

Track density (number of cyclone tracks per 1° × 1° area per 10 winter seasons) for (a) current climate, (b) future climate, and (c) difference between future and current simulations (future − current). Contours are color shaded every 1 count in (a) and (b) and every 0.5 count in (c). Differences in (c) contoured and shaded in green are statistically significant at the 95% confidence level.

Fig. 4.

Track density (number of cyclone tracks per 1° × 1° area per 10 winter seasons) for (a) current climate, (b) future climate, and (c) difference between future and current simulations (future − current). Contours are color shaded every 1 count in (a) and (b) and every 0.5 count in (c). Differences in (c) contoured and shaded in green are statistically significant at the 95% confidence level.

b. Cyclone intensity

By definition, strong and moderate cyclones in the present-day simulations comprise ~5% and ~90% of the total number of storms within the North Atlantic storm-track region, respectively. Using the same intensity thresholds for the future simulations shows a substantial reduction in the frequency of storms reaching a minimum SLP perturbation of at least −51 hPa (Table 1). This indicates strong storms occur less often in our future simulations, which is consistent with previous work (e.g., Chang 2014). Examination of current and future composites of strong and moderate storms show future storms exhibit very small changes in average minimum SLP perturbation (~1 hPa). These results suggest that warming does not have a strong impact on the average intensity of strong or moderate storms within the North Atlantic storm-track region, but does influence their frequency of occurrence.

Table 1.

Number of storms and percent of total storms categorized as strong and moderate within the storm-track region in current and future simulations.

Number of storms and percent of total storms categorized as strong and moderate within the storm-track region in current and future simulations.
Number of storms and percent of total storms categorized as strong and moderate within the storm-track region in current and future simulations.

The Eady growth rate maximum can be used to evaluate changes in baroclinicity. As shown for these simulations by W15, the difference in average Eady growth rate over the 850–500-hPa layer exhibits a slight increase over the storm-track area. Portions of this area of increase correlate with areas of increased cyclogenesis and 6-hourly deepening rates greater than 5 hPa (not shown). These changes, however, are small, indicating that the baroclinic environment within the storm-track region is not significantly more favorable for cyclone development in a warmer climate.

c. PV framework

In the PV view of extratropical cyclones, there are four important PV features: 1) the surface-based warm anomaly, 2) the upper-level cyclonic PV typically associated with an upper trough, 3) the diabatically generated lower-tropospheric cyclonic PV, and 4) the diabatically generated upper-level anticyclonic PV (Davis and Emanuel 1991; Reed et al. 1992; Brennan et al. 2008; Lackmann 2011). Using this framework, cyclogenesis can be viewed as interactions between these features. The present analysis is focused on the three cyclonic components of this framework.

1) Surface-based warm anomaly

As previously mentioned, the surface-based warm anomaly is represented by the 2-m potential temperature anomaly . Generally, the current and future for strong storms within the main storm-track region exhibit similar structures at all times with a maxima near the cyclone center extending outward, intensifying and shifting from southeast to northeast over the 24-h period (Fig. 5). While the for the future composite is slightly stronger over the 24-h period, the current composite shows strengthening at a faster rate. For example, between 24 h prior to peak intensity and 12 h prior to peak intensity, the maximum for the current composite increases by 0.5 K while the maximum for the future composite increases by less than 0.1 K. Over the 24-h period, the average over the compositing area and the maximum increase about 5% (Table 2); however, these changes are not found to be statistically significant at the 95% confidence level using a Wilcoxon rank-sum test.

Fig. 5.

Storm-relative composites of (K) for strong storms within the storm-track region for (a)–(c) current and (d)–(f) future simulations at (left) 24 h prior to peak intensity, (center) 12 h prior to peak intensity, and (right) time of peak intensity. Contours are color shaded every 0.5 K. The composite storm center is represented by an asterisk. Distances from the storm center in grid points are shown on the ordinate and abscissa. Note that 25 grid points correspond to roughly 500 km.

Fig. 5.

Storm-relative composites of (K) for strong storms within the storm-track region for (a)–(c) current and (d)–(f) future simulations at (left) 24 h prior to peak intensity, (center) 12 h prior to peak intensity, and (right) time of peak intensity. Contours are color shaded every 0.5 K. The composite storm center is represented by an asterisk. Distances from the storm center in grid points are shown on the ordinate and abscissa. Note that 25 grid points correspond to roughly 500 km.

Table 2.

Average percent changes in average and maximum values over the compositing area between future and current climates for strong and moderate storms occurring within the storm-track region.

Average percent changes in average and maximum values over the compositing area between future and current climates for strong and moderate storms occurring within the storm-track region.
Average percent changes in average and maximum values over the compositing area between future and current climates for strong and moderate storms occurring within the storm-track region.

As with strong storms, the structure of the is similar between current and future moderate storms within the main storm track region; a maximum is located near the cyclone center extending out to the southeast at 24 h prior to peak intensity. Over the 24-h period, this maximum strengthens and rotates northward. The gradient to the west of the cyclone center also strengthens in both composites over the 24-h period (Figs. 6a–f). However, unlike strong storms, the is weaker in the future composite for moderate storms, especially at later times (Figs. 6g–i). Again, using a Wilcoxon rank-sum test, a portion of the changes seen in the for moderate storms, particularly the reduction at the time of peak intensity, are found to be statistically significant at the 95% confidence level (Figs. 6g–i). For moderate storms, the average over the compositing area decreases about 14% and the maximum decreases about 10% (Table 2).

Fig. 6.

Storm-relative composites of (K) for moderate storms within the storm-track region for (a)–(c) current and (d)–(f) future simulations and (g)–(i) difference between future and current climates (future - current) at (left) 24 h prior to peak intensity, (center) 12 h prior to peak intensity, and (right) time of peak intensity. Contours are shaded every 0.5 K in (a)–(f) and every 0.25 K in (g)–(i). Areas in (g)–(i) contoured and shaded in green are statistically significant at the 95% confidence level. The composite storm center is represented by an asterisk. Distances from the storm center in grid points are shown on the ordinate and abscissa. Note that 25 grid points correspond to roughly 500 km.

Fig. 6.

Storm-relative composites of (K) for moderate storms within the storm-track region for (a)–(c) current and (d)–(f) future simulations and (g)–(i) difference between future and current climates (future - current) at (left) 24 h prior to peak intensity, (center) 12 h prior to peak intensity, and (right) time of peak intensity. Contours are shaded every 0.5 K in (a)–(f) and every 0.25 K in (g)–(i). Areas in (g)–(i) contoured and shaded in green are statistically significant at the 95% confidence level. The composite storm center is represented by an asterisk. Distances from the storm center in grid points are shown on the ordinate and abscissa. Note that 25 grid points correspond to roughly 500 km.

2) UPV

As previously discussed, the UPV is taken as the 300–200-hPa layer average PV. The composited UPV for current and future strong storms, as well as the change in UPV with warming, is shown in Fig. 7. At 24 h prior to peak intensity, a maximum in UPV is evident to the west of the cyclone center in both current and future composites. This maximum is oriented more to the north and the gradient to the east of the cyclone center is stronger in the future composite (Figs. 7a,d). The structure of UPV 12 and 24 h later is similar between current and future composites with a maximum to the south-southwest of the cyclone center; this maximum is stronger in the current composite (Figs. 7b,c,e,f). The overall character of the difference fields (Figs. 7g–i) indicates a reduction in the strength of the UPV with warming. Portions of the reduction to the northeast of the storm center at 12 h prior to peak intensity (Fig. 7h) as well as the reduction to the northwest of the storm center at the time of peak intensity (Fig. 7i) are found to be statistically significant at the 95% confidence level. Over all composite times, the average UPV over the composite area decreases about 4% with warming. The maximum UPV shows virtually no change with less than a 1% increase in the future simulation (Table 2).

Fig. 7.

As in Fig. 6, but for storm-relative composites of UPV (PVU) for strong storms within the storm-track region. Contours are shaded every 0.5 PVU in (a)–(f) and every 0.1 PVU in (g)–(i).

Fig. 7.

As in Fig. 6, but for storm-relative composites of UPV (PVU) for strong storms within the storm-track region. Contours are shaded every 0.5 PVU in (a)–(f) and every 0.1 PVU in (g)–(i).

The structure of UPV between the current and future composites for moderate storms within the main storm-track region is very similar with a maximum to the north-northeast of the cyclone center (Figs. 8a–f). The reductions in UPV shown for future storms (Figs. 8g–i) appear to be due to a tightening of the UPV gradient around the storm center. Over the 24-h period, the average UPV over the composite area decreases by approximately 1% and the maximum UPV decreases by less than 1% due to warming (Table 2). While this overall decrease is small, large portions of the reductions in UPV seen at all compositing times are found to be statistically significant at the 95% confidence level (Figs. 8g–i).

Fig. 8.

As in Fig, 7, but for moderate storms.

Fig. 8.

As in Fig, 7, but for moderate storms.

3) Precipitation and lower-tropospheric cyclonic PV

An increase in water vapor is expected to occur with a warming climate, which is likely to result in an increase in precipitation intensity (e.g., Trenberth 1999; Allen and Ingram 2002). Composites of 6-hourly precipitation show an area of heavy precipitation concentrated around the storm center at 24 h prior to peak intensity and 12 h prior to peak intensity in both current and future composites (Figs. 9a,b,d,e), albeit this area of precipitation is greater in the future composite (Figs. 9g–h), consistent with this expectation. There is also an increase in precipitation evident in the future composite along the expected warm frontal region to the north of the storm center at the time of peak intensity (Figs. 9c,f,i). A subset of these changes, namely the increase around the storm center 24 h prior to peak intensity (Fig. 9g) and the increase to the northeast of the storm center at the time of peak intensity (Fig. 9i), are found to be statistically significant at the 95% confidence level. Over all composite times, the average 6-h precipitation over the composite area and the maximum 6-h precipitation increase by about 18% and 37%, respectively (Table 2). The average 850-hPa temperature over the composite area (not shown) increases by ~3.3°K, corresponding to a ~23% increase in water vapor according to the Clausius–Clapeyron relation. Therefore, the average precipitation in the future is found to increase at a sub-Clausius–Clapeyron rate.

Fig. 9.

As in Fig. 6, but for storm-relative composites of 6-hourly precipitation (mm) for strong storms within the storm-track region. Contours are shaded every 1 mm in (a)–(i).

Fig. 9.

As in Fig. 6, but for storm-relative composites of 6-hourly precipitation (mm) for strong storms within the storm-track region. Contours are shaded every 1 mm in (a)–(i).

The increase in precipitation seen for future strong storms suggests a subsequent increase in latent heat release with warming, which is consistent with the increase in the diabatically generated lower-tropospheric PV (DPV), taken as the 900–700-hPa layer average PV, shown in Fig. 10. While both current and future composited storms show a maximum of DPV located over the storm center (Figs. 10a–f), the strength of this DPV is greater in the future composite (Figs. 10g–i). Most increases in DPV seen for strong storms are found to be statistically significant at the 95% confidence level (Figs. 10g–i). Over the 24-h period, the average DPV over the composite area increases by approximately 19% and the maximum DPV increases by about 36% from current to future climates (Table 2).

Fig. 10.

As in Fig. 6, but for DPV (PVU) for strong storms within the storm-track region. Contours are shaded every 0.1 PVU in (a)–(i).

Fig. 10.

As in Fig. 6, but for DPV (PVU) for strong storms within the storm-track region. Contours are shaded every 0.1 PVU in (a)–(i).

Similar to strong cyclones, moderate storms show an area of heavy precipitation and DPV concentrated around the cyclone center (Figs. 11a–f and 12a–f). Future moderate storms also exhibit an increase in 6-hourly precipitation (Figs. 11g–i), as well as an increase in DPV (Figs. 12g–i), at all composite times. These increases are also largely found to be statistically significant at the 95% confidence level (Figs. 11g–i and 12g–i). Over the 24-h period, the average and maximum 6-h precipitation over the composite area increase by about 24% and 15%, respectively. The average and maximum DPV increase by approximately 6% and 10%, respectively (Table 2). The average 850-hPa temperature over the composite area for moderate storms (not shown) increases by about 3.9 K, which suggests a ~27% increase in water vapor. Therefore, as with strong storms, the average precipitation increase for moderate storms is slightly less than the increase in water vapor dictated by the Clausius–Clapeyron relation.

Fig. 11.

As in Fig. 9, but for moderate storms. Contours are shaded every 0.5 mm in (a)–(f) and every 0.25 mm in (g)–(i).

Fig. 11.

As in Fig. 9, but for moderate storms. Contours are shaded every 0.5 mm in (a)–(f) and every 0.25 mm in (g)–(i).

Fig. 12.

As in Fig. 10, but for moderate storms. Contours are shaded every 0.1 PVU in (a)–(f) and every 0.025 PVU in (g)–(i).

Fig. 12.

As in Fig. 10, but for moderate storms. Contours are shaded every 0.1 PVU in (a)–(f) and every 0.025 PVU in (g)–(i).

4. Discussion

Storm-relative composites of both strong and moderate storms within the North Atlantic storm-track region show a significant increase in 6-hourly precipitation, which is consistent with the increase in moisture expected under climate change conditions and stronger forcing for ascent due to strengthened 850-hPa temperature advection aided by enhanced 850-hPa wind speed in the approximate vicinity of the warm front to the northeast of the storm center (Fig. 13). Increased precipitation results in enhanced latent heat release, which contributes to the strengthening of DPV shown for both strong and moderate cyclones, a positive feedback that has been documented in previous studies. This enhancement of DPV could be acting to erode the UPV (Plant et al. 2003), resulting in the reduction of UPV seen for strong and moderate future storms. Increased DPV also suggests a possible strengthening of the low-level jet, which implies stronger low-level winds. For strong storms, enhanced low-level winds at the time of peak intensity are primarily evident north-northeast of the cyclone center, in the expected location of the warm front (Fig. 14a). Maximum and average 10-m wind speeds over the compositing area at this time show an increase of approximately 6% and 2%, respectively. With the exception of an area directly north of the storm center, enhanced low-level winds are also seen for moderate cyclones at the time of peak intensity (Fig. 14b). Both average and maximum 10-m wind speeds over the compositing area for moderate storms at this time show an increase of ~(3%–4%). Therefore, evidence suggests stronger low-level winds are associated with future storms within the main storm-track region. These regions of enhanced lower-tropospheric winds are also consistent with areas of strengthened SLP gradient (not shown). Additionally, the future simulations feature more than 100 additional instances of gale-force [34 kt (17.5 m s−1)] winds over much of the North Atlantic. A slight increase in the occurrence of gale-force winds is also evident off the coasts of the northeast United States and parts of western Europe while a minor reduction is apparent off the southeast U.S. coast and west of the British Isles (Fig. 15a).

Fig. 13.

Storm-relative composite of the difference in (a) total 850-hPa wind speed (m s−1) and (b) 850-hPa temperature advection (10−4 K s−1) between future and current climates (future − current) for strong storms 12 h prior to peak intensity. Contours are shaded every 0.5 m s−1 in (a) and every 0.25 × 10−4 K s−1 in (b). Other features are as in Fig. 6.

Fig. 13.

Storm-relative composite of the difference in (a) total 850-hPa wind speed (m s−1) and (b) 850-hPa temperature advection (10−4 K s−1) between future and current climates (future − current) for strong storms 12 h prior to peak intensity. Contours are shaded every 0.5 m s−1 in (a) and every 0.25 × 10−4 K s−1 in (b). Other features are as in Fig. 6.

Fig. 14.

Storm-relative composites of the difference in the total 10-m wind speed between future and current climates (future − current) for (a) strong and (b) moderate storms at the time of peak intensity. Contours are shaded every 0.25 m s−1 in (a) and every 0.1 m s−1 in (b). Other features are as in Fig. 6.

Fig. 14.

Storm-relative composites of the difference in the total 10-m wind speed between future and current climates (future − current) for (a) strong and (b) moderate storms at the time of peak intensity. Contours are shaded every 0.25 m s−1 in (a) and every 0.1 m s−1 in (b). Other features are as in Fig. 6.

Fig. 15.

Difference between future and current simulations (future − current) in the number of times a grid point exceeded (a) 10-m wind speed of 34 kt (=17.5 m s−1 = 39 mph) and (b) a 6-hourly precipitation rate of 38 mm (1.5 in.) over the 10 winter seasons. Contours are shaded every 15 in (a) and every 1 in (b).

Fig. 15.

Difference between future and current simulations (future − current) in the number of times a grid point exceeded (a) 10-m wind speed of 34 kt (=17.5 m s−1 = 39 mph) and (b) a 6-hourly precipitation rate of 38 mm (1.5 in.) over the 10 winter seasons. Contours are shaded every 15 in (a) and every 1 in (b).

Storms producing heavy precipitation are also shown to occur more frequently in the future simulations. Out of the storms from the future simulation included in the composite, about 71% had at least one grid point exceed a 6-hourly rain rate of 38 mm (~1.5 in.) during the analysis period compared to ~43% of the present-day composited storms. The same trend is seen for moderate storms (Table 3). In terms of spatial extent of heavy precipitation, the average area per strong storm exceeding 38 mm of rain in 6 h increases by approximately 57%. Therefore, strong storms producing heavy rainfall occur more often, and this rainfall affects a larger area in the future simulations. While moderate storms producing heavy rainfall are also shown to become more frequent, the coverage of heavy precipitation slightly decreases by ~6% (Table 3). When the precipitation threshold is changed to 25 mm (~1 in.), however, the coverage of heavy precipitation for moderate storms increases. Over the southeast United States in particular, the occurrences of 6-hourly rain rates ≥38 mm (1.5 in.) is shown to increase by 8–15 times in most areas (Fig. 15b).

Table 3.

Percent of composited storms with at least one grid point exceeding a 6-hourly rain rate of 38 mm (~1.5 in.) during the analysis period in the top row and average area (km2) per storm over the analysis period exceeding this threshold in the bottom row.

Percent of composited storms with at least one grid point exceeding a 6-hourly rain rate of 38 mm (~1.5 in.) during the analysis period in the top row and average area (km2) per storm over the analysis period exceeding this threshold in the bottom row.
Percent of composited storms with at least one grid point exceeding a 6-hourly rain rate of 38 mm (~1.5 in.) during the analysis period in the top row and average area (km2) per storm over the analysis period exceeding this threshold in the bottom row.

Another way to visualize the changes in the vertical PV structure seen in future storms is with cross-sections of quasigeostrophic PV (QGPV) through the composite storms. Here, we rescaled the original Ertel PV following QG assumptions to achieve the QGPV form (Hoskins et al. 1985, section 5b); this allows PV anomalies to become more apparent, especially at upper levels. Figure 16 shows southwest–northeast cross sections of QGPV through strong and moderate storms 12 h prior to peak intensity in current and future climates. Consistent with the results presented in section 3, both strong and moderate storms show a clear amplification of DPV at lower levels. In regard to UPV, there is a clear reduction for strong storms (Figs. 16a,b), but no obvious change is evident for moderate storms (Figs. 16c,d). These changes in storm dynamics are similar to what Marciano et al. (2015) found for a subset of strong storms off the U.S. East Coast. The cases in Marciano et al. (2015), however, were shown to primarily increase in intensity, whereas the storms in the present study do not exhibit large changes in intensity (e.g., minimum SLP perturbation) with warming.

Fig. 16.

Southwest–northeast cross sections of QGPV (10−4 s−1) through a composite storm for (a),(b) strong storms in the current and climate simulation, respectively; and (c),(d) moderate storms in the current climate and future simulation, respectively. Contours are shaded every 0.5 × 10−4 s−1. Noise near the surface in (a),(b) is due to the surface pressures of strong cyclones being <1000 mb (1 mb = 1 hPa).

Fig. 16.

Southwest–northeast cross sections of QGPV (10−4 s−1) through a composite storm for (a),(b) strong storms in the current and climate simulation, respectively; and (c),(d) moderate storms in the current climate and future simulation, respectively. Contours are shaded every 0.5 × 10−4 s−1. Noise near the surface in (a),(b) is due to the surface pressures of strong cyclones being <1000 mb (1 mb = 1 hPa).

The area of maximum increase of 300-hPa eddy kinetic energy and 850-hPa eddy heat flux shown by W15 occurs to the south and east of the increase in track density shown in Fig. 4c. We hypothesize that as cyclones propagate poleward, the processes contributing to enhancing these quantities occur farther south than the cyclone center itself. Examination of the difference in 850-hPa height variance between the future and current simulations (not shown) supports this hypothesis.

5. Conclusions

Overall, we find a systematic increase in cyclone track density with warming over the northern portion of the North Atlantic storm track, with decreases to the south (Fig. 4). Other subregional changes include enhanced cyclone activity immediately to the east of the U.S. East Coast, as well as reduced activity in the southwest North Atlantic and over the western Mediterranean region (Fig. 4c). In contrast to the findings of Colle et al. (2013), this increase off the U.S. East Coast was not found to be statistically significant. These changes in cyclone activity do not necessarily correspond to the changes in Eulerian quantities presented by W15; thus, additional, societally relevant, information has been obtained by applying feature-tracking methods to these simulations. With regard to storm intensity, examination of cyclones within the main storm track shows a shift toward fewer strong storms in the future climate simulations (Table 1).

Our results show that climate change can influence the storm-scale dynamics of extratropical cyclones throughout the North Atlantic storm-track region. An increase in the , albeit not statistically significant, and a reduction in UPV are seen for strong storms, while small changes in UPV and a weakened are found for moderate storms. Both strong and moderate cyclones exhibit enhanced 6-hourly precipitation. Similar to Marciano et al. (2015) the average precipitation is found to increase at a sub-Clausius–Clapeyron rate. Amplified DPV is also shown for strong and moderate storms, indicating that storms are potentially more diabatically driven in a warmer climate; this suggests a possible shift toward more frequent type C cyclones in the classification system of Plant et al. (2003). This evidence is further supported by the increase in cyclone activity found in the eastern Atlantic, shown by Dacre and Gray (2009) to be a region conducive to type C cyclones. Previous work has discussed a potential sensitivity of type C cyclones to climate change owing to their reliance on latent heating for intensification (W15), and therefore, should be explored further, potentially by repeating the present analysis while utilizing a PV-based classification.

Although strong storms are shown to become less frequent in a warmer climate (Table 1), some impacts (e.g., precipitation and low-level winds) associated with storms in this region are found to intensify. For one, storms are associated with more precipitation under future climate conditions (Figs. 9 and 11). Cyclones associated with 6-hourly rain rates greater than 38 mm (1.5 in.) are also shown to occur more frequently and with greater areal coverage (Table 3). Enhanced precipitation, along with increased cyclone activity over the northeast North Atlantic, could pose problems for highly populated areas such as the British Isles and western Europe. More frequent heavy rain rates over the southeast United States (Fig. 15b) also suggest an increased potential for inland flooding. Additionally, stronger low-level winds and more frequent gale-force winds (Figs. 13, 14, and 15a) could increase the risk of wind damage or maritime hazards associated with extratropical cyclones in this region. This, along with projected sea level rise, could imply a higher risk of, and more dangerous, storm surges.

As shown by W13 and W15, coarse model resolutions are unable to adequately resolve diabatic feedbacks in the North Atlantic storm track. The present study shows these processes play a larger role in the intensification of future storms; therefore, it is vital that climate models and future modeling studies be conducted at sufficient resolutions to better capture their effects. Recent work by Willison (2015) suggests potential limitations for conducting the present analysis using a regional domain. Therefore, future work will involve reconciling the results presented herein with those from simulations of the winters of 2002–11 in current and future climates using a global model configuration.

Acknowledgments

This research was supported by NSF Grants AGS-1007606 and AGS-1546743, awarded to North Carolina State University. The WRF Model and NCAR Command Language (NCL) were made available by the National Center for Atmospheric Research, sponsored by the National Science Foundation. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) is acknowledged for making the general circulation model (GCM) output used in this study available. The authors would also like to thank three anonymous reviewers for their constructive comments and valuable feedback on earlier versions of this manuscript.

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Footnotes

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